Category Archives: Reading Group

Deep object detection – Reading session

by Zhiming Luo


  1. R-CNN

Girshick et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” CVPR. 2014.

  1. SPP-Net

He et al. “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.” TPAMI. 2015.

  1. Fast RCNN, OHEM

Girshick. “Fast R-CNN.” ICCV. 2015.

Shrivastava et al. “Training Region-based Object Detectors with Online Hard Example Mining.” CVPR. 2016.

  1. Faster RCNN:

Ren et al. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” NIPS. 2015.

Zhang et al. “Is Faster R-CNN Doing Well for Pedestrian Detection.” ECCV. 2016.

  1. YOLO, SSD

Redmon et al. “You Only Look Once: Unified, Real-Time Object Detection.” CVPR. 2016.

Liu et al. “SSD: Single Shot MultiBox Detector.” ECCV. 2016.


Python Multiprocessing

Introduction by Martin:

This Friday’s reading group will be a hands-on tutorial on multiprocessing in Python. Please make sure to have Python 2 installed on your laptop. No further preparation needed, if you want an IDE: Spyder is decent. Slides.

Topics discussed:

  • CPU count
  • Process class
  • Queues
  • Locks
  • Pool class
  • Hands-on challenge!

Recurrent Neural Network and LSTMs

A different formula will be attempted for this reading group: everyone shall each study their own paper on the subject of Recurrent Neural Networks (RNN) or Long Short-Term Memory (LSTM), and present it.

List of the available papers

Presented papers:



Introduction by Martin:

The next reading group will be on MRI. Since this is quite a general subject, instead of reading papers, I will make you watch a few introduction videos on selected key topics. I have included questions for each video to make sure you understood what you just watched. You are expected to watch them again or do some of your own research if you have absolutely no clue (they are short videos after all). We’ll go through them on Friday and some more advanced topics if we have time.

Continue reading MRI


Motion Detection

Introduction by Yi:

Since some of you mentioned to me that they would like to know more about motion detection, the subject of the next reading group would be about this topic. At the same time, not everyone in the lab is working on motion detection, I’d like to make the reading group general instead of too specific. That means most of the stuffs would be about the basic ideas about motion detection, the widely-used methods, features, background model updating strategies and so on.

Required reading

Take-home message:

  • Still an open question, not one method is better than the other and there is always room for improvement. Combining different methods is still a hot topic.